Suitability analysis is the process and procedures used to establish the suitability of a system – that is, the ability of a system to meet the needs of a stakeholder or other user.
Before GIS (a computerized method that helps to determine suitability analysis) was widely used in the mid to late 20th century, city planners communicated their suitability analysis ideas by laying transparencies in increasing darkness over maps of the present conditions. This technique's descendant is used in a GIS application called multicriteria decision analysis.[1] In the 1960s, a mechanism called the ecological inventory process was developed to document existing surrounding land conditions to help inform the analysis for the land in question. These mechanisms were computerized upon the advent of computers due to inefficiencies in the methods, such as the inability to overlay a large number of transparencies.[2]
In order to feed a growing population that is pushing on the ability to extensively farm, suitability analysis is becoming more necessary to utilize the most productive land to its fullest potential, matching the needs of the plants more carefully to the existing assets in the environment. This technique is known as precision farming.[3]
Suitability analysis can also be used to track and label potential hazards, like earthquakes, contamination, or even crime. It can also be used to locate advantageous locations for commercial centers.[4]
A suitability model is a model that weights locations relative to each other based on given criteria. Suitability models might aid in finding a favorable location for a new facility, road, or habitat for a species of bird.[5] Overlay analysis is a common method for creating a suitability model which involves using GIS techniques and software.[6] Overlay techniques were originally advanced by Ian McHarg, who used a manual overlay cartographic process which he describes in his 1969 book Design with Nature.[7] With the advancement of computer mapping software, suitability modeling has become much easier and faster to implement, and today it is used for many varying tasks.
There are seven general steps required to create an acceptable suitability model:
Without a clear understanding of the problem that needs to be solved a suitability model cannot be successful. All other steps in the process will contribute to the objective of solving this problem. The components of this objective should also be defined, as well as a way of knowing when the problem has been solved. Considering the issue of deforestation, to lower deforestation rates a suitability model could be created to model areas most likely to be deforested in the immediate future; laws and regulating entities could then be focused on those areas most susceptible to deforestation. The overall goal of the deforestation suitability model would be to slow the rate of deforestation.
The complexity of most suitability modeling problems can be overwhelming and confusing; for this reason, it is advisable to break the model into submodels. For deforestation there are many different drivers, therefore, a variety of submodels would be needed. Population, population density, movement of people, elevation, slope, land cover type, hydrology, location of protected areas, soil type, laws, roads and infrastructure, the list could go on, all of these things affect where deforestation happens and the intensity. Combining these factors could lead to a submodel for physical environment (elevation, slope, land cover, land use, soil type, and hydrology), for built environment (roads, infrastructure, and other relevant transportation networks), and for demographic characteristics (population, population density, population growth rate, and poverty rate).[9]
Each submodel should be defining an aspect of the overall model, and only submodel factors which contribute to solving the original problem should be included in a submodel. It is in this step that data must be gathered, and layers created; for example, it may be known that deforestation usually happens a certain distance from city/road/agricultural areas, therefore a Euclidean distance tool (within a GIS software package) could be used to create a distance raster around these areas.
There are many different datasets going into the model, all with varying number systems; this means that attempting to combine these datasets would give meaningless results. Therefore, a common number scale should be chosen (usually 1 to 9 for a weighted overlay and 0 to 1 for a fuzzy overlay; with larger values signifying more favorable areas) and each dataset reclassified to the new scale (there should be a tool for this in most GIS applications).[10]
If there is strong evidence that some factors contribute more to the main goal these factors should be weighted based on their level of contribution. For instance, focusing specifically on deforestation in Africa, previous research shows that one of the main causes of deforestation is fuel wood extraction; therefore, variables associated with fuel wood extraction should be weighted more heavily than other variables.[11] Weighting should not be done if a fuzzy overlay is used.
To complete the model, all factors must be combined, usually through a weighted overlay or fuzzy overlay technique. For a weighted overlay all the factors would be added together and reclassified to form a new data layer where high values signify more favorable locations and low values less favorable locations. A fuzzy overlay analysis produces the same type of results but through more complex methods.
Once the suitability model is complete the results should be analyzed. It is always a good idea to examine the results closely to verify that they make sense, and no mistakes were made. Before the model is used the results should also be verified and validated. Ideally, the value of predictive methods based in habitat suitability to estimate for instance the population size of common species should be tested before conducting large-scale monitoring, rather than a posteriori. Although logistically challenging, this can be achieved by designing monitoring programs including an intensive sampling of abundance in ad hoc reference areas of variable size.[12] After the analysis is complete locations can be selected using the model and this information can be applied to the original problem.
Suitability analysis in a GIS context is a geographic, or GIS-based process used to determine the appropriateness of a given area for a particular use. The basic premise of GIS suitability analysis is that each aspect of the landscape has intrinsic characteristics that are to some degree either suitable or unsuitable for the activities being planned. Suitability is determined through systematic, multi-factor analysis of the different aspects of the terrain.[13] Model inputs include a variety of physical, cultural, and economic factors. The results are often displayed on a map that is used to highlight areas from high to low suitability.[14]
A GIS suitability model typically answers the question, "Where is the best location?" — whether it involves finding the best location for a new road or pipeline, a new housing development, or a retail store. For instance, a commercial developer building a new retail store may take into consideration distance to major highways and any competitors' stores, then combine the results with land use, population density, and consumer spending data to decide on the best location for that store.[15]
The possibility space is a framework that allows for the analysis of all possible consequences and benefits of a suitability analysis. This is created through geometrical data analysis conducted in real time with technological land mapping, allowing for the development of multiple combinations of suitability. Physically it is a visual interactive database that allows for a holistic composition of suitability.[20]
When suitability analyses are done, several different usability options may be found for the same section of land. This can be advantageous or limiting. If the land is found suitable for two or more uses that can be combined, the land uses are found compatible. An example of this may be a building with businesses on the bottom floor with residences on upper floors. Compatible land uses result in a win-win development; a need for more commerce is met while meeting a need for more housing, while also keeping people on the street all day, thereby reducing the probability of crime. Conflicting land use occurs when a piece of land can be used only for one use or the other. This is exemplified by a piece of land that can either be used as agricultural land or developed into a housing tract—should the land be developed; it can no longer be used for agriculture. The suitability analysis comes back into play here by helping planners prioritize which need is greater (in the case of the example, is housing or agricultural land more necessary in light of economic or demand pressure).[22]